Early Prediction for Graduation of Private High School Students with Machine Learning Approach

Kartini,; Fetty Tri Anggraeny,; Aang Kisnu Darmawan,; Anik Anekawati,; Ivana Yudhisari,

doi:10.47577/technium.v16i.9971

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…While certain measures such as adjusting passing thresholds or attendance tracking might seem to bolster graduation rates, concerns linger regarding their impact on the overall quality of graduates [6]. Hence, the ability to identify students at risk of delayed graduation becomes imperative, facilitating proactive interventions to support them and uphold the quality of graduates [7,8].…”

Section: Introductionmentioning

confidence: 99%

Ensemble-SMOTE: Mitigating Class Imbalance in Graduate on Time Detection

Law,

Ting,

et al. 2024

JIWE

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In education, detecting students graduating on time is difficult due to high data complexity. Researchers have employed various approaches in identifying on-time graduation with Machine Learning, but it remains a challenging task due to the class imbalance in the dataset. This study has aimed to (i) compare various class imbalance treatment methods with different sampling ratios, (ii) propose an ensemble class imbalance treatment method in mitigating the problem of class imbalance, and (iii) develop and evaluate predictive models in identifying the likelihood of students graduating on time during their studies in university. The dataset is collected from 4007 graduates of a university from year 2021 and 2022 with 41 variables. After feature selection, various class imbalance treatment methods were compared with different sampling ratios ranging from 50% to 90%. Moreover, Ensemble-SMOTE is proposed to aggregate the dataset generated by Synthetic Minority Oversampling Technique variants in mitigating the problem of class imbalance effectively. The dataset generated by class imbalance treatment methods were used as the input of the predictive models in detecting on-time graduation. The predictive models were evaluated based on accuracy, precision, recall, F0.5-score, F1-score, F2-score, Area under the Curve, and Area Under the Precision-Recall Curve. Based on the findings, Logistic Regression with Ensemble-SMOTE outperformed other predictive models, and class imbalance treatment methods by achieving the highest average accuracy (87.24), recall (92.50%), F1-score (91.30%), and F2-score (92.02%) from 6th until 10th trimester. To assess the effectiveness of class imbalance treatment methods, Friedman test is performed to determine on significant difference between the models after applying Shapiro-Wilk test in normality test. Consequently, Ensemble-SMOTE is ranked as the top-performers by achieving the lowest value in the average rank based on the performance metrics. Additional research could incorporate and examine more complicated approaches in mitigating class imbalance when the dataset is highly imbalanced.

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Section: Introductionmentioning

confidence: 99%

Ensemble-SMOTE: Mitigating Class Imbalance in Graduate on Time Detection

Law,

Ting,

et al. 2024

JIWE

View full text Add to dashboard Cite

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Student Attendance Patterns as Actionable Early Warning Indicators of High School Graduation Outcomes: Findings from an Urban Alternative Charter School

Marshall

2024

Urban Science

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Background: Students who fail to complete high school, on average, have less earnings, pay fewer taxes, and require increased government expenditures compared to those who do reach this milestone. Especially in urban jurisdictions, this can lead to reduced municipal fiscal health. Alternative high schools have been one intervention employed to improve the outcomes of students who have previously dropped out. Also, in recent decades, early warning indicator (EWI) systems have been put in place to flag students who are at risk for not completing school. However, the current EWI metrics for student attendance are insufficient for the population that attends alternative high schools. Methods: Administrative data were obtained from an alternative charter high school in a large urban city in the northeast United States (n = 224). Logistic regression analyses were conducted to test the utility of traditional EWI operationalizations of student attendance against more targeted measures, with student graduation serving as a dichotomous outcome variable. Results: Of the models tested, the model that flagged student non-attendance as missing consecutive days three or more times during the first 12 weeks of school had the greatest explanatory power (McFadden’s R2 = 0.301) and best overall model fit. Conclusions: Traditional definitions of attendance are less useful in schools doing re-engagement work, and more targeted indicators serve as more effective EWIs in these settings.

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Early Prediction for Graduation of Private High School Students with Machine Learning Approach

Cited by 2 publications

References 17 publications

Ensemble-SMOTE: Mitigating Class Imbalance in Graduate on Time Detection

Ensemble-SMOTE: Mitigating Class Imbalance in Graduate on Time Detection

Student Attendance Patterns as Actionable Early Warning Indicators of High School Graduation Outcomes: Findings from an Urban Alternative Charter School

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